Python

4 reasons you’ll love using Red Hat OpenShift Data Science

4 reasons you’ll love using Red Hat OpenShift Data Science

Red Hat OpenShift Data Science is a managed cloud service built from a curated set of components from the upstream Open Data Hub project. It aims to provide a stable sandbox in which data scientists can develop, train, and test their machine learning (ML) workloads and then deploy results in a container-ready format. This article summarizes the advantages of using OpenShift Data Science in your machine learning projects.

Continue reading 4 reasons you’ll love using Red Hat OpenShift Data Science

Share
Using the SystemTap Dyninst runtime environment

Using the SystemTap Dyninst runtime environment

SystemTap (stap) uses a command-line interface (CLI) and a scripting language to write instrumentation for a live running kernel or a user space application. A SystemTap script associates handlers with named events. This means, when a specified event occurs, the default SystemTap kernel runtime runs the handler in the kernel as if it is a quick subroutine, and then it resumes.

Continue reading Using the SystemTap Dyninst runtime environment

Share
Write your own Red Hat Ansible Tower inventory plugin

Write your own Red Hat Ansible Tower inventory plugin

Ansible is an engine and language for automating many different IT tasks, such as provisioning a physical device, creating a virtual machine, or configuring an application and its dependencies. Ansible organizes these tasks in playbook files, which run on one or more remote target hosts. Inventory files maintain lists of these hosts and are formatted as YAML or INI documents. For example, a simple inventory file in INI format follows:

Continue reading Write your own Red Hat Ansible Tower inventory plugin

Share
Knowledge meets machine learning for smarter decisions, Part 1

Knowledge meets machine learning for smarter decisions, Part 1

Drools is a popular open source project known for its powerful rules engine. Few users realize that it can also be a gateway to the amazing possibilities of artificial intelligence. This two-part article introduces you to using Red Hat Decision Manager and its Drools-based rules engine to combine machine learning predictions with deterministic reasoning. In Part 1, we’ll prepare our machine learning logic. In Part 2, you’ll learn how to use the machine learning model from a knowledge service.

Continue reading Knowledge meets machine learning for smarter decisions, Part 1

Share
Use Kebechet machine learning to perform source code operations

Use Kebechet machine learning to perform source code operations

One of the first tools we developed to help us with Project Thoth was Kebechet, which we named for the goddess of freshness and purification. As we separated our software into more and more repositories (each of our Python modules is in its own repository on GitHub), we needed help with releasing new versions and keeping all dependent modules up-to-date. In a team of two and with more than 35 repositories, our process was a major time-burner.

Continue reading Use Kebechet machine learning to perform source code operations

Share
Kubernetes integration and more in odo 2.0

Kubernetes integration and more in odo 2.0

Odo is a developer-focused command-line interface (CLI) for OpenShift and Kubernetes. This article introduces highlights of the odo 2.0 release, which now integrates with Kubernetes. Additional highlights include the new default deployment method in odo 2.0, which uses devfiles for rapid, iterative development. We’ve also moved Operator deployment out of experimental mode, so you can easily deploy Operator-backed services from the odo command line.

Continue reading “Kubernetes integration and more in odo 2.0”

Share